{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K-均值聚类算法"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "K-均值聚类支持函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from numpy import *\n",
    "\n",
    "def loadDataSet(fileName):      #general function to parse tab -delimited floats\n",
    "    dataMat = []                #assume last column is target value\n",
    "    fr = open(fileName)\n",
    "    for line in fr.readlines():\n",
    "        curLine = line.strip().split('\\t')\n",
    "        fltLine = list(map(float,curLine)) #map all elements to float()\n",
    "        dataMat.append(fltLine)\n",
    "    return dataMat\n",
    "\n",
    "def distEclud(vecA, vecB):\n",
    "    return sqrt(sum(power(vecA - vecB, 2))) #la.norm(vecA-vecB)\n",
    "\n",
    "def randCent(dataSet, k):\n",
    "    n = shape(dataSet)[1]\n",
    "    centroids = mat(zeros((k,n)))#create centroid mat\n",
    "    for j in range(n):#create random cluster centers, within bounds of each dimension\n",
    "        minJ = min(dataSet[:,j]) \n",
    "        rangeJ = float(max(dataSet[:,j]) - minJ)\n",
    "        centroids[:,j] = mat(minJ + rangeJ * random.rand(k,1))\n",
    "    return centroids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.7422928 ],\n",
       "       [ 0.81796631]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random.rand(2,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[ 1.658985,  4.285136],\n",
       "        [-3.453687,  3.424321],\n",
       "        [ 4.838138, -1.151539],\n",
       "        [-5.379713, -3.362104],\n",
       "        [ 0.972564,  2.924086],\n",
       "        [-3.567919,  1.531611],\n",
       "        [ 0.450614, -3.302219],\n",
       "        [-3.487105, -1.724432],\n",
       "        [ 2.668759,  1.594842],\n",
       "        [-3.156485,  3.191137],\n",
       "        [ 3.165506, -3.999838],\n",
       "        [-2.786837, -3.099354],\n",
       "        [ 4.208187,  2.984927],\n",
       "        [-2.123337,  2.943366],\n",
       "        [ 0.704199, -0.479481],\n",
       "        [-0.39237 , -3.963704],\n",
       "        [ 2.831667,  1.574018],\n",
       "        [-0.790153,  3.343144],\n",
       "        [ 2.943496, -3.357075],\n",
       "        [-3.195883, -2.283926],\n",
       "        [ 2.336445,  2.875106],\n",
       "        [-1.786345,  2.554248],\n",
       "        [ 2.190101, -1.90602 ],\n",
       "        [-3.403367, -2.778288],\n",
       "        [ 1.778124,  3.880832],\n",
       "        [-1.688346,  2.230267],\n",
       "        [ 2.592976, -2.054368],\n",
       "        [-4.007257, -3.207066],\n",
       "        [ 2.257734,  3.387564],\n",
       "        [-2.679011,  0.785119],\n",
       "        [ 0.939512, -4.023563],\n",
       "        [-3.674424, -2.261084],\n",
       "        [ 2.046259,  2.735279],\n",
       "        [-3.18947 ,  1.780269],\n",
       "        [ 4.372646, -0.822248],\n",
       "        [-2.579316, -3.497576],\n",
       "        [ 1.889034,  5.1904  ],\n",
       "        [-0.798747,  2.185588],\n",
       "        [ 2.83652 , -2.658556],\n",
       "        [-3.837877, -3.253815],\n",
       "        [ 2.096701,  3.886007],\n",
       "        [-2.709034,  2.923887],\n",
       "        [ 3.367037, -3.184789],\n",
       "        [-2.121479, -4.232586],\n",
       "        [ 2.329546,  3.179764],\n",
       "        [-3.284816,  3.273099],\n",
       "        [ 3.091414, -3.815232],\n",
       "        [-3.762093, -2.432191],\n",
       "        [ 3.542056,  2.778832],\n",
       "        [-1.736822,  4.241041],\n",
       "        [ 2.127073, -2.98368 ],\n",
       "        [-4.323818, -3.938116],\n",
       "        [ 3.792121,  5.135768],\n",
       "        [-4.786473,  3.358547],\n",
       "        [ 2.624081, -3.260715],\n",
       "        [-4.009299, -2.978115],\n",
       "        [ 2.493525,  1.96371 ],\n",
       "        [-2.513661,  2.642162],\n",
       "        [ 1.864375, -3.176309],\n",
       "        [-3.171184, -3.572452],\n",
       "        [ 2.89422 ,  2.489128],\n",
       "        [-2.562539,  2.884438],\n",
       "        [ 3.491078, -3.947487],\n",
       "        [-2.565729, -2.012114],\n",
       "        [ 3.332948,  3.983102],\n",
       "        [-1.616805,  3.573188],\n",
       "        [ 2.280615, -2.559444],\n",
       "        [-2.651229, -3.103198],\n",
       "        [ 2.321395,  3.154987],\n",
       "        [-1.685703,  2.939697],\n",
       "        [ 3.031012, -3.620252],\n",
       "        [-4.599622, -2.185829],\n",
       "        [ 4.196223,  1.126677],\n",
       "        [-2.133863,  3.093686],\n",
       "        [ 4.668892, -2.562705],\n",
       "        [-2.793241, -2.149706],\n",
       "        [ 2.884105,  3.043438],\n",
       "        [-2.967647,  2.848696],\n",
       "        [ 4.479332, -1.764772],\n",
       "        [-4.905566, -2.91107 ]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datMat = mat(loadDataSet('testSet.txt'))\n",
    "datMat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-5.379713]]\n",
      "[[ 4.838138]]\n"
     ]
    }
   ],
   "source": [
    "print(min(datMat[:, 0]))\n",
    "print(max(datMat[:, 0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-4.232586]]\n",
      "[[ 5.1904]]\n"
     ]
    }
   ],
   "source": [
    "print(min(datMat[:, 1]))\n",
    "print(max(datMat[:, 1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[-4.61822298,  1.91098465],\n",
       "        [-3.648383  ,  1.70711223]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "randCent(datMat,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.184632816681332"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distEclud(datMat[0],datMat[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "K-均值聚类算法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def kMeans(dataSet, k, distMeas=distEclud, createCent=randCent):\n",
    "    m = shape(dataSet)[0]\n",
    "    clusterAssment = mat(zeros((m,2)))#create mat to assign data points \n",
    "                                      #to a centroid, also holds SE of each point\n",
    "    centroids = createCent(dataSet, k)\n",
    "    clusterChanged = True\n",
    "    while clusterChanged:\n",
    "        clusterChanged = False\n",
    "        for i in range(m):#for each data point assign it to the closest centroid\n",
    "            minDist = inf; minIndex = -1\n",
    "            for j in range(k):\n",
    "                distJI = distMeas(centroids[j,:],dataSet[i,:])\n",
    "                if distJI < minDist:\n",
    "                    minDist = distJI; minIndex = j\n",
    "            if clusterAssment[i,0] != minIndex: clusterChanged = True\n",
    "            clusterAssment[i,:] = minIndex,minDist**2\n",
    "        print(centroids)\n",
    "        for cent in range(k):#recalculate centroids\n",
    "            ptsInClust = dataSet[nonzero(clusterAssment[:,0].A==cent)[0]]#get all the point in this cluster\n",
    "            centroids[cent,:] = mean(ptsInClust, axis=0) #assign centroid to mean \n",
    "    return centroids, clusterAssment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-4.87166305 -0.2484721 ]\n",
      " [ 0.81844991  4.77895674]\n",
      " [-0.04742617  4.54418785]\n",
      " [ 4.65603974 -0.79108414]]\n",
      "[[-3.41126175 -2.14546583]\n",
      " [ 2.41411012  3.43082119]\n",
      " [-2.18799937  3.01824781]\n",
      " [ 2.91514388 -1.9729095 ]]\n",
      "[[-3.38237045 -2.9473363 ]\n",
      " [ 2.6265299   3.10868015]\n",
      " [-2.46154315  2.78737555]\n",
      " [ 2.80293085 -2.7315146 ]]\n"
     ]
    }
   ],
   "source": [
    "myCentroids, clustAssing = kMeans(datMat, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[-3.38237045, -2.9473363 ],\n",
       "        [ 2.6265299 ,  3.10868015],\n",
       "        [-2.46154315,  2.78737555],\n",
       "        [ 2.80293085, -2.7315146 ]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myCentroids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[  1.00000000e+00,   2.32019150e+00],\n",
       "        [  2.00000000e+00,   1.39004893e+00],\n",
       "        [  3.00000000e+00,   6.63839104e+00],\n",
       "        [  0.00000000e+00,   4.16140951e+00],\n",
       "        [  1.00000000e+00,   2.76967820e+00],\n",
       "        [  2.00000000e+00,   2.80101213e+00],\n",
       "        [  3.00000000e+00,   5.85909807e+00],\n",
       "        [  0.00000000e+00,   1.50646425e+00],\n",
       "        [  1.00000000e+00,   2.29348924e+00],\n",
       "        [  2.00000000e+00,   6.45967483e-01],\n",
       "        [  3.00000000e+00,   1.74010499e+00],\n",
       "        [  0.00000000e+00,   3.77769471e-01],\n",
       "        [  1.00000000e+00,   2.51695402e+00],\n",
       "        [  2.00000000e+00,   1.38716420e-01],\n",
       "        [  3.00000000e+00,   9.47633071e+00],\n",
       "        [  0.00000000e+00,   9.97310599e+00],\n",
       "        [  1.00000000e+00,   2.39726914e+00],\n",
       "        [  2.00000000e+00,   3.10242360e+00],\n",
       "        [  3.00000000e+00,   4.11084375e-01],\n",
       "        [  0.00000000e+00,   4.74890795e-01],\n",
       "        [  1.00000000e+00,   1.38706133e-01],\n",
       "        [  2.00000000e+00,   5.10240996e-01],\n",
       "        [  3.00000000e+00,   1.05700176e+00],\n",
       "        [  0.00000000e+00,   2.90181828e-02],\n",
       "        [  1.00000000e+00,   1.31601105e+00],\n",
       "        [  2.00000000e+00,   9.08203769e-01],\n",
       "        [  3.00000000e+00,   5.02608557e-01],\n",
       "        [  0.00000000e+00,   4.57942717e-01],\n",
       "        [  1.00000000e+00,   2.13786618e-01],\n",
       "        [  2.00000000e+00,   4.05632356e+00],\n",
       "        [  3.00000000e+00,   5.14171888e+00],\n",
       "        [  0.00000000e+00,   5.56237495e-01],\n",
       "        [  1.00000000e+00,   4.76142736e-01],\n",
       "        [  2.00000000e+00,   1.54414110e+00],\n",
       "        [  3.00000000e+00,   6.10930460e+00],\n",
       "        [  0.00000000e+00,   9.47660177e-01],\n",
       "        [  1.00000000e+00,   4.87745774e+00],\n",
       "        [  2.00000000e+00,   3.12703929e+00],\n",
       "        [  3.00000000e+00,   6.45118831e-03],\n",
       "        [  0.00000000e+00,   3.01415411e-01],\n",
       "        [  1.00000000e+00,   8.84955695e-01],\n",
       "        [  2.00000000e+00,   7.98870968e-02],\n",
       "        [  3.00000000e+00,   5.23673430e-01],\n",
       "        [  0.00000000e+00,   3.24171404e+00],\n",
       "        [  1.00000000e+00,   9.32523506e-02],\n",
       "        [  2.00000000e+00,   9.13705455e-01],\n",
       "        [  3.00000000e+00,   1.25766593e+00],\n",
       "        [  0.00000000e+00,   4.09563895e-01],\n",
       "        [  1.00000000e+00,   9.46987842e-01],\n",
       "        [  2.00000000e+00,   2.63836399e+00],\n",
       "        [  3.00000000e+00,   5.20371222e-01],\n",
       "        [  0.00000000e+00,   1.86796790e+00],\n",
       "        [  1.00000000e+00,   5.46768776e+00],\n",
       "        [  2.00000000e+00,   5.73153563e+00],\n",
       "        [  3.00000000e+00,   3.12040332e-01],\n",
       "        [  0.00000000e+00,   3.93986735e-01],\n",
       "        [  1.00000000e+00,   1.32864695e+00],\n",
       "        [  2.00000000e+00,   2.38032454e-02],\n",
       "        [  3.00000000e+00,   1.07872914e+00],\n",
       "        [  0.00000000e+00,   4.35369355e-01],\n",
       "        [  1.00000000e+00,   4.55502856e-01],\n",
       "        [  2.00000000e+00,   1.96212809e-02],\n",
       "        [  3.00000000e+00,   1.95213538e+00],\n",
       "        [  0.00000000e+00,   1.54154401e+00],\n",
       "        [  1.00000000e+00,   1.26364010e+00],\n",
       "        [  2.00000000e+00,   1.33108375e+00],\n",
       "        [  3.00000000e+00,   3.02422139e-01],\n",
       "        [  0.00000000e+00,   5.58860689e-01],\n",
       "        [  1.00000000e+00,   9.52516316e-02],\n",
       "        [  2.00000000e+00,   6.25129762e-01],\n",
       "        [  3.00000000e+00,   8.41875177e-01],\n",
       "        [  0.00000000e+00,   2.06159470e+00],\n",
       "        [  1.00000000e+00,   6.39227291e+00],\n",
       "        [  2.00000000e+00,   2.01200372e-01],\n",
       "        [  3.00000000e+00,   3.51030769e+00],\n",
       "        [  0.00000000e+00,   9.83287604e-01],\n",
       "        [  1.00000000e+00,   7.06014703e-02],\n",
       "        [  2.00000000e+00,   2.59901305e-01],\n",
       "        [  3.00000000e+00,   3.74491207e+00],\n",
       "        [  0.00000000e+00,   2.32143993e+00]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clustAssing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "绘制图形"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "def showPlt(datMat, alg=kMeans, numClust=4):\n",
    "    myCentroids, clustAssing = alg(datMat, numClust)\n",
    "    fig = plt.figure()\n",
    "    rect=[0.1,0.1,0.8,0.8]\n",
    "    scatterMarkers=['s', 'o', '^', '8', 'p', \\\n",
    "                    'd', 'v', 'h', '>', '<']\n",
    "    axprops = dict(xticks=[], yticks=[])\n",
    "    ax0=fig.add_axes(rect, label='ax0', **axprops)\n",
    "    ax1=fig.add_axes(rect, label='ax1', frameon=False)\n",
    "    for i in range(numClust):\n",
    "        ptsInCurrCluster = datMat[nonzero(clustAssing[:,0].A==i)[0],:]\n",
    "        markerStyle = scatterMarkers[i % len(scatterMarkers)]\n",
    "        ax1.scatter(ptsInCurrCluster[:,0].flatten().A[0], ptsInCurrCluster[:,1].flatten().A[0], marker=markerStyle, s=90)\n",
    "    ax1.scatter(myCentroids[:,0].flatten().A[0], myCentroids[:,1].flatten().A[0], marker='+', s=300)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.67711867  2.40616063]\n",
      " [ 4.2928946  -2.23364616]\n",
      " [-0.57609435 -1.92512698]\n",
      " [-5.07760659 -1.06728796]]\n",
      "[[ 0.07030082  3.03323226]\n",
      " [ 3.23113972 -2.53879733]\n",
      " [-1.54246    -3.03963209]\n",
      " [-4.01184177 -2.44495577]]\n",
      "[[ 0.0469085   3.05287679]\n",
      " [ 3.09814284 -2.43041226]\n",
      " [-1.46335425 -3.40428925]\n",
      " [-3.81529733 -2.550205  ]]\n",
      "[[ 0.0469085   3.05287679]\n",
      " [ 3.09814284 -2.43041226]\n",
      " [-1.05904467 -3.687141  ]\n",
      " [-3.68129565 -2.55085547]]\n",
      "[[ 0.0469085   3.05287679]\n",
      " [ 3.09814284 -2.43041226]\n",
      " [-0.28093075 -3.880518  ]\n",
      " [-3.56908268 -2.62975353]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x80e05f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "showPlt(datMat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### 二分 K-均值算法"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "二分K-均值聚类算法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def biKmeans(dataSet, k, distMeas=distEclud):\n",
    "    m = shape(dataSet)[0]\n",
    "    clusterAssment = mat(zeros((m,2)))\n",
    "    centroid0 = mean(dataSet, axis=0).tolist()[0]\n",
    "    centList =[centroid0] #create a list with one centroid\n",
    "    for j in range(m):#calc initial Error\n",
    "        clusterAssment[j,1] = distMeas(mat(centroid0), dataSet[j,:])**2\n",
    "    while (len(centList) < k):\n",
    "        lowestSSE = inf\n",
    "        for i in range(len(centList)):\n",
    "            ptsInCurrCluster = dataSet[nonzero(clusterAssment[:,0].A==i)[0],:]#get the data points currently in cluster i\n",
    "            centroidMat, splitClustAss = kMeans(ptsInCurrCluster, 2, distMeas)\n",
    "            sseSplit = sum(splitClustAss[:,1])#compare the SSE to the currrent minimum\n",
    "            sseNotSplit = sum(clusterAssment[nonzero(clusterAssment[:,0].A!=i)[0],1])\n",
    "            print(\"sseSplit, and notSplit: \",sseSplit,sseNotSplit)\n",
    "            if (sseSplit + sseNotSplit) < lowestSSE:\n",
    "                bestCentToSplit = i\n",
    "                bestNewCents = centroidMat\n",
    "                bestClustAss = splitClustAss.copy()\n",
    "                lowestSSE = sseSplit + sseNotSplit\n",
    "        bestClustAss[nonzero(bestClustAss[:,0].A == 1)[0],0] = len(centList) #change 1 to 3,4, or whatever\n",
    "        bestClustAss[nonzero(bestClustAss[:,0].A == 0)[0],0] = bestCentToSplit\n",
    "        print('the bestCentToSplit is: ',bestCentToSplit)\n",
    "        print('the len of bestClustAss is: ', len(bestClustAss))\n",
    "        centList[bestCentToSplit] = bestNewCents[0,:].tolist()[0]#replace a centroid with two best centroids \n",
    "        centList.append(bestNewCents[1,:].tolist()[0])\n",
    "        clusterAssment[nonzero(clusterAssment[:,0].A == bestCentToSplit)[0],:]= bestClustAss#reassign new clusters, and SSE\n",
    "    return mat(centList), clusterAssment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "datMat3=mat(loadDataSet('testSet2.txt'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.56867914  4.1601344 ]\n",
      " [ 2.82120977  2.41100798]]\n",
      "[[-1.62922896  3.46284893]\n",
      " [ 1.04623688 -0.60536709]]\n",
      "[[-1.47714714  3.46545543]\n",
      " [ 0.99677359 -0.73477953]]\n",
      "[[-1.3277349   3.46607079]\n",
      " [ 0.93680474 -0.87084665]]\n",
      "[[-0.84735206  3.46862312]\n",
      " [ 0.63042504 -1.33843332]]\n",
      "[[-0.26853357  3.36606168]\n",
      " [ 0.02053813 -2.21845543]]\n",
      "[[-0.06953469  3.29844341]\n",
      " [-0.32150057 -2.62473743]]\n",
      "[[-0.00675605  3.22710297]\n",
      " [-0.45965615 -2.7782156 ]]\n",
      "sseSplit, and notSplit:  453.033489581 0.0\n",
      "the bestCentToSplit is:  0\n",
      "the len of bestClustAss is:  60\n",
      "[[ 3.7079305   2.82180407]\n",
      " [-3.36465997  2.73168804]]\n",
      "[[ 2.93386365  3.12782785]\n",
      " [-2.94737575  3.3263781 ]]\n",
      "sseSplit, and notSplit:  77.5922493178 29.1572494441\n",
      "[[ 0.39454802 -3.75379694]\n",
      " [ 0.04072578 -3.19883248]]\n",
      "[[ 0.57536475 -3.65629275]\n",
      " [-0.71841137 -2.55869631]]\n",
      "[[ 0.408857   -3.54908071]\n",
      " [-0.92731708 -2.36313438]]\n",
      "[[ 0.35496167 -3.36033556]\n",
      " [-1.12616164 -2.30193564]]\n",
      "sseSplit, and notSplit:  12.7532631369 423.876240137\n",
      "the bestCentToSplit is:  0\n",
      "the len of bestClustAss is:  40\n"
     ]
    }
   ],
   "source": [
    "centList,myNewAssments=biKmeans(datMat3, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[ 2.93386365,  3.12782785],\n",
       "        [-0.45965615, -2.7782156 ],\n",
       "        [-2.94737575,  3.3263781 ]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "centList"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.83970567  1.6258321 ]\n",
      " [ 1.38640649  1.22429594]]\n",
      "[[-2.30113848  1.32923452]\n",
      " [ 2.13351476  1.1142599 ]]\n",
      "[[-2.24671409  1.16767909]\n",
      " [ 2.22969593  1.291217  ]]\n",
      "[[-2.11802947  0.88737776]\n",
      " [ 2.40575527  1.66726781]]\n",
      "[[-1.70351595  0.27408125]\n",
      " [ 2.93386365  3.12782785]]\n",
      "sseSplit, and notSplit:  541.297629265 0.0\n",
      "the bestCentToSplit is:  0\n",
      "the len of bestClustAss is:  60\n",
      "[[ -4.72017480e+00   3.10484814e+00]\n",
      " [ -4.16018549e-03  -3.41445306e+00]]\n",
      "[[-2.94737575  3.3263781 ]\n",
      " [-0.45965615 -2.7782156 ]]\n",
      "sseSplit, and notSplit:  67.2202000798 39.5292986821\n",
      "[[ 2.58443261  0.81211618]\n",
      " [ 2.23530829  1.45643753]]\n",
      "[[ 2.441611    0.444826  ]\n",
      " [ 2.95977168  3.26903847]]\n",
      "sseSplit, and notSplit:  31.6968654205 501.768330583\n",
      "the bestCentToSplit is:  0\n",
      "the len of bestClustAss is:  40\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x829bf98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "showPlt(datMat3, alg=biKmeans, numClust=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 示例：对地图上的点进行聚类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import urllib\n",
    "import json\n",
    "def geoGrab(stAddress, city):\n",
    "    apiStem = 'http://where.yahooapis.com/geocode?'  #create a dict and constants for the goecoder\n",
    "    params = {}\n",
    "    params['flags'] = 'J'#JSON return type\n",
    "    params['appid'] = 'aaa0VN6k'\n",
    "    params['location'] = '%s %s' % (stAddress, city)\n",
    "    url_params = urllib.parse.urlencode(params)\n",
    "    yahooApi = apiStem + url_params      #print url_params\n",
    "    print(yahooApi)\n",
    "    c=urllib.request.urlopen(yahooApi)\n",
    "    return json.loads(c.read())\n",
    "\n",
    "from time import sleep\n",
    "def massPlaceFind(fileName):\n",
    "    fw = open('places.txt', 'w')\n",
    "    for line in open(fileName).readlines():\n",
    "        line = line.strip()\n",
    "        lineArr = line.split('\\t')\n",
    "        retDict = geoGrab(lineArr[1], lineArr[2])\n",
    "        if retDict['ResultSet']['Error'] == 0:\n",
    "            lat = float(retDict['ResultSet']['Results'][0]['latitude'])\n",
    "            lng = float(retDict['ResultSet']['Results'][0]['longitude'])\n",
    "            print(\"%s\\t%f\\t%f\" % (lineArr[0], lat, lng))\n",
    "            fw.write('%s\\t%f\\t%f\\n' % (line, lat, lng))\n",
    "        else: print(\"error fetching\")\n",
    "        sleep(1)\n",
    "    fw.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#geoResults = geoGrab('1 VA Center', 'Augusta ME')  #stop working as yahoo API not available any more"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def distSLC(vecA, vecB):#Spherical Law of Cosines\n",
    "    a = sin(vecA[0,1]*pi/180) * sin(vecB[0,1]*pi/180)\n",
    "    b = cos(vecA[0,1]*pi/180) * cos(vecB[0,1]*pi/180) * \\\n",
    "                      cos(pi * (vecB[0,0]-vecA[0,0]) /180)\n",
    "    return arccos(a + b)*6371.0 #pi is imported with numpy\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "def clusterClubs(numClust=5):\n",
    "    datList = []\n",
    "    for line in open('places.txt').readlines():\n",
    "        lineArr = line.split('\\t')\n",
    "        datList.append([float(lineArr[4]), float(lineArr[3])])\n",
    "    datMat = mat(datList)\n",
    "    myCentroids, clustAssing = biKmeans(datMat, numClust, distMeas=distSLC)\n",
    "    fig = plt.figure()\n",
    "    rect=[0.1,0.1,0.8,0.8]\n",
    "    scatterMarkers=['s', 'o', '^', '8', 'p', \\\n",
    "                    'd', 'v', 'h', '>', '<']\n",
    "    axprops = dict(xticks=[], yticks=[])\n",
    "    ax0=fig.add_axes(rect, label='ax0', **axprops)\n",
    "    imgP = plt.imread('Portland.png')\n",
    "    ax0.imshow(imgP)\n",
    "    ax1=fig.add_axes(rect, label='ax1', frameon=False)\n",
    "    for i in range(numClust):\n",
    "        ptsInCurrCluster = datMat[nonzero(clustAssing[:,0].A==i)[0],:]\n",
    "        markerStyle = scatterMarkers[i % len(scatterMarkers)]\n",
    "        ax1.scatter(ptsInCurrCluster[:,0].flatten().A[0], ptsInCurrCluster[:,1].flatten().A[0], marker=markerStyle, s=90)\n",
    "    ax1.scatter(myCentroids[:,0].flatten().A[0], myCentroids[:,1].flatten().A[0], marker='+', s=300)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-122.64564199   45.48172111]\n",
      " [-122.4421615    45.60277358]]\n",
      "[[-122.64310229   45.51342982]\n",
      " [-122.44600225   45.494056  ]]\n",
      "[[-122.66149437   45.51440946]\n",
      " [-122.50322869   45.50324862]]\n",
      "[[-122.67273274   45.51397274]\n",
      " [-122.52363268   45.50792237]]\n",
      "[[-122.68917857   45.50793286]\n",
      " [-122.54222807   45.51911044]]\n",
      "[[-122.69551477   45.50729503]\n",
      " [-122.54868607   45.51882187]]\n",
      "sseSplit, and notSplit:  3043.26331611 0.0\n",
      "the bestCentToSplit is:  0\n",
      "the len of bestClustAss is:  69\n",
      "[[-122.7189039    45.50621166]\n",
      " [-122.78876066   45.39202492]]\n",
      "[[-122.69406936   45.51602683]\n",
      " [-122.71285967   45.40251333]]\n",
      "[[-122.69359691   45.52297633]\n",
      " [-122.706063     45.42104783]]\n",
      "[[-122.69634517   45.52889607]\n",
      " [-122.69274678   45.43529156]]\n",
      "sseSplit, and notSplit:  1441.2311748 851.438888582\n",
      "[[-122.55746767   45.4777832 ]\n",
      " [-122.43611743   45.5530545 ]]\n",
      "[[-122.56448358   45.522632  ]\n",
      " [-122.44600225   45.494056  ]]\n",
      "[[-122.56706156   45.52335936]\n",
      " [-122.4568086    45.4961344 ]]\n",
      "sseSplit, and notSplit:  505.619608202 2191.82442752\n",
      "the bestCentToSplit is:  0\n",
      "the len of bestClustAss is:  39\n",
      "[[-122.79981076   45.62441609]\n",
      " [-122.71227778   45.56658723]]\n",
      "[[-122.842918     45.646831  ]\n",
      " [-122.69129093   45.52482934]]\n",
      "sseSplit, and notSplit:  891.143107471 1089.08472991\n",
      "[[-122.44515724   45.5217923 ]\n",
      " [-122.52622833   45.49604711]]\n",
      "[[-122.42811233   45.485694  ]\n",
      " [-122.56208315   45.52250274]]\n",
      "sseSplit, and notSplit:  511.806537619 1441.2311748\n",
      "[[-122.68756008   45.43240228]\n",
      " [-122.63230821   45.39566035]]\n",
      "[[-122.711667     45.44164029]\n",
      " [-122.626526     45.413071  ]]\n",
      "[[-122.72380383   45.43821817]\n",
      " [-122.63063267   45.42943833]]\n",
      "[[-122.7365076    45.4329942 ]\n",
      " [-122.63804575   45.43816325]]\n",
      "sseSplit, and notSplit:  105.710518984 2055.02422206\n",
      "the bestCentToSplit is:  1\n",
      "the len of bestClustAss is:  30\n",
      "[[-122.7677709    45.49040384]\n",
      " [-122.67428109   45.59746052]]\n",
      "[[-122.704075     45.50489771]\n",
      " [-122.68623692   45.56027854]]\n",
      "[[-122.69539585   45.5064217 ]\n",
      " [-122.6982438    45.5738448 ]]\n",
      "[[-122.68892548   45.51026343]\n",
      " [-122.72072414   45.59011757]]\n",
      "[[-122.69025475   45.51162812]\n",
      " [-122.72070683   45.59796783]]\n",
      "sseSplit, and notSplit:  734.222834844 749.452378946\n",
      "[[-122.42978945   45.47132289]\n",
      " [-122.41938365   45.44964998]]\n",
      "[[-122.4540165   45.5133815]\n",
      " [-122.376304    45.430319 ]]\n",
      "sseSplit, and notSplit:  10.6582807474 1861.02534226\n",
      "[[-122.76312173   45.38876662]\n",
      " [-122.65707265   45.45901489]]\n",
      "[[-122.759843     45.404235  ]\n",
      " [-122.67357643   45.44416486]]\n",
      "sseSplit, and notSplit:  136.452176428 1715.39187109\n",
      "[[-122.52227189   45.55623396]\n",
      " [-122.57496681   45.50816704]]\n",
      "[[-122.56762175   45.549179  ]\n",
      " [-122.55975111   45.51127063]]\n",
      "[[-122.5848452    45.5546092 ]\n",
      " [-122.54869371   45.50361659]]\n",
      "sseSplit, and notSplit:  167.47806397 1533.24354496\n",
      "the bestCentToSplit is:  0\n",
      "the len of bestClustAss is:  30\n"
     ]
    },
    {
     "data": {
      "image/png": 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t30eSpSRFFPhBKOYIiJJEEPgosoJ+Bu33JE7qZ52E47rMzGP5nbiWEcwZXN/z\nOWy0KORypwQYYxWB+drVst6PZerDEDpL+Xz2lHFIp3V29w7JplOIkpQc12cFf6eMx7d+/2tH3/7m\nd/+EkFl1Et/71u9/7WJUjggH9UaSo3Qiqe+qsMabH7554dcYz8K872v330oMyPr66pm0XFgcRrRs\nKNOj5GRjyrDv+8iKTDGTYhQpwaYez4PnLyjtLkM+nw09aknjViGMNEa2w2okID7f6FdZKdFu97j1\nCPx7CD9vRVYwZjMURaZcKvLK57eQgtCbc90wDXevcdzJ3zzcZzPnc/vOXRRFwXEcnnn68ZCSa8yQ\nJYnt7XXqh010XVvoDWi3e/h+QEAoiVJdKYdGNpeh3x+iaiq5XAbXden2BoiCgCSJyWKqKjJpXYsk\naEZsb6yFhioIJwN+9YXHeOMNAcsCy4LHboQMpFwuy2AwYnt7nf2DI0a2wlsPGtSKMo1GmyAIzhXa\nvAz0VA5fltm5toEiazhuGO3Ef2t6BsucoqfCyNGcjdm5toEgZygUjl9HUXUkScGMru+jo1YSFeip\nHOZsvPA7fi0IUziqqoRNiHKGwJ2e0paazSxSKY3ZzCSV0k8pSA+HYyZTI5Go35iTfCkW8+HQq+ge\ni43FarWMqqpMJlMUWUoMn6apSyOak7BtG9/3IpmZ42N92L471zbwPZ+9gyNKpTx+8NkpmP+dMh4A\n3/r9r/32+VtdDMuigoP6Ys7/l5/+ZQC+f+f7C4+vFdZoDptUc1Xa49M50L2DozM9v3gY0VGzfaHu\n14tCFAVESUY/QYRRKhrjt3vkXjqfSXV41GJz8ynUzhvcWvnCQ7etVsu0Wt1Hoip2e+GAoxvXt8Ii\ncxAw7IdzPeLIwjBMflp32MmOufXE40lUsbO9sRCxxYYiLmgHQbAgo9FodrAdl82NVUajCaqm4Xke\n1Wo5KdTurJTCCMOYYVnHqSxZkuh0+6iqgjFrUS4VkSQp2U+SRPA8mo1DDg42+fzLE8rpLL7nM55M\nKJeLrFTCHP77LQFw+doLa8lCuoz1dDJquOg0SkXVaQ1t9mcv0HvX4ctPpdlriRz2bMo5gRdu6IyM\nAE2Ed3anXF/Tee2uz997OodpB/iBzvt1k5duqLx7YNMaGrx0M8u9oxnb1W0aI5/u2AUmPFbTeefe\nhM9tiXh+wI8+DCnCAC88VsSwPN5vwI2ay0o2Ta/XRpKOl6tWu0s1mo0xnRrMTAtREHFch3K5SKGQ\nWzAmg8Fb3R19AAAgAElEQVSI4WhEOp0JDQPgeh7j0ZTeoE+xUGAQFfDnP7uDeoPN9fPvsUazQ221\nAoKQGLaLIp4NA8faa58FfHaO9DOCrc3NU4+9/sHrbJaXGwI3WF4kOy9lUD9arqr7URCnW2RZ5uio\nldQzRF1GUI8vlYdpV8U9BnblJdTe2+e+5+pq5ZFqNOHQrLCfQ1VkAt+nUMija2EBt9cfJiJ8thcu\nqJl0ClVVqB82Oag32N07ZDKdJQVxWZJoNDt4no9phXnyyXRGqZCjVltFlmXK5SKKoiSLg+/76JqG\nZVpIopDUN2RZRhJFVFVhpVJCibqw02kdXVPJplPsXNsglw2FKtdqG7z4wgO2VtL8zTt3+Zt32tRq\n4Rjc/++nLb7zZhhtvPK5lXPVXasnBg4NR5OkN2F37/DMIvhoBtWcjOX4PP9YhtfvThgaHo9vpHC9\nsBfmJw8mSJLKYc/m394ZcWMt/BwKGYkffWjwxVs6E1vk2Z0MLz+R5d0Dgy/cytIeODQHx+nUDxuh\n0Xv9vpfMC3npRpjWFEWS11otqBizCdlshkazTSqlkUpp7FzbYDo1UBSZTCZNbW2FXD5DuVyM2GfW\nQmG6WMyzc20L13E4arRxHBdREMjlM/h+yDTbubaREBbguLAdj56N02zzhe24R0SRJOpRZ/9lDEds\n/LOZNPXDJp8hdRKEsxgMVziNF25lg7/+49996Dav3X8rrHkcvZ/UPB6GcrbMUe+IbHabtWw6kW6H\nRan3+cl/7U7/TEbJR8GyAmlsKCY/7aFWU8l42ocxpmLPV/QMpMkeTuGph77vReSuY9y99yBJNx3U\nG2QzaTzXw/E8SpGnqekajUYbQRDZ73uMDYtfe2k7eY1Go02tVg1HvLoetm0vGGLXddk/aJDNZpKF\n1vXcJAUCx9pSccG5kM8u1A8azU44GzzwcT0PWZLCpj/AcR0UWVlIL9brzXDRETTeun9ayuIrTxWZ\nGTMEUXhoGmR37wBN08lmM4nkfKPZIZ/LYppm5KEL+EGAqqpLryO19zZ2+fmHfQ0JFFXHsZcXji/T\nc2QYoUbXsrTNeDQll8+wu3fASqX80BGwl0E8EnY+Ujs5b2c8mjKZTk89Fkc4vudj2fZDRy1cBO12\nD2Nm8p/91/+cH30w/UzII/6dS1t9kmg0O2HdIgh4pnZ6SFCM1/pv8XLpuEC+lg09xXu9Ebm5RTmV\n0pKFNZtJh3njUmHhho8Xwk8C+/tH3Npe515vdCyWGBmPe73RmQYkvhF9KY2grSBP93Ez20u3hdCz\nu+ic9mIhn0iVCIIQSXmksZ1QGDA2sKVyCU2VcdwGQ+OEXPpcPlrXVCqlwsLToVaTFCngWvSH40Ts\ncGrMqJSLdDo9BEHAsp2oviExHk/RdY3BYMRPdod845Xn+XC3gSCH3afJ28saLlBvzjHu5AwzB1ar\neUQ6+Bwf8689H4rt9foDZFleiNR830/SJKIYzj1xXRfbsujOTJyo473d6aFFEigIAlldQ5JExqMp\n6YzGeGwgy1JY1BcvJi8PLDUclmnR7vaplM92cAI/WEjNptP6mfn+uIlvnm78cSBOES3TLFNkhVw+\nk/wsO57YqJ1nOC7CWovraxed+f5pwJXx+JhwkQmBMT6ff/qUAQEo+N7Cohynj+LXra1VE657nL/P\n5jJnsjw+Kk7q8aSuZxl874jir54/uzo2Bp6+gmR2zjUgW5u1CzF8qtVyMmxnJemClrAnBtlseiGC\nOag3GFoy4GLZLtOohhD4PpZpLXRta1KojjubmeiaSrEQPqfpGrVofjiEApKWHTKHhqMJiiJTrZRo\nRPpEUqSo+o1XnufP/9/z03YncXu3SSGjM5xOEeUMLz5ZZL/eCOeNiCLZjJoc90G9QUoL0ziO45FO\nKxQKedqdXtiAGBmyyfS4hySOWnq9Abqm4vkBlhXKwRdyOQ7qDR47W5H+TNTrYTe95/ts1FbPdATi\nAU2CKPz8hyNFl8r8NSOK0oWK4xAakYvUk2LD0Wh2ME0LTVWQo4Flo8mE1WoF1/WYzWZ/Z5oErxBh\nMp2d2aS1DCoqa+oqbzQXexbjiOIg6gE5qDcWDFKsQnpQb1BbW6G2tpIMdfo44CyZgHYw348iiWjb\nmYW+j7OwtVlLdLj6dqQYaj68FyBeTM6aAR1jfa3KcDgO01OtLo7j4kT9H+1On4N6g1YrzGsXNJcv\nPLnKa+92se2wMczz/WSxB5hMjGhmuZ0MdjIMk/phk929w4iqqVAs5pEkEWNq4PlBskBOjVmiTFvI\n58hmM3y4GxbfU7rGr37xKb7xSpgGihsWAW7Vjj3zX/3iU8nv4Vy/Sjlz/N1ubiw6J1ubNSorJTzP\nR1HCpknHcZIpf7t7hwiCgBd5vsbMZDAY0e30UTUNUZJod3o0Wx0kUaQ3GIbqsI7D0VFraTH+LGxu\nrrG+HhqNeLGc7/r+SIhVqedS7Gc1JlqmRavVZTgcMxiM2N8/otvpL3TCx+d1Vs3wvMbIeWi6trTh\ndxlqaytc39kMWZnVMrl8hs2NtUSB96JG69OCK+PxCLBMi5lhMjPMkKedSV3Ii4rZPKIkspPZ5NmV\nJ3it/1ZyMdcPm9wq56mmdO71RpipMAUTy5DEN2XspS+b8PZR0FmyaJsnbozUzTxOx8Qdhsf8MAMS\na21lM2nczDaevoLe/sG5x1EuFxfF6uZgmRaiJFI/DD/LQj7LaDTGisbeVldKpFM6aV3nxvUtJFGk\nmBHZqmYwZo9BEKDIysL3VVkpJd782uoKkizhei6ZdIpcNk0qpSe1p1gJtrpSwjQtBEFYmMLoeR66\nplKKuvS//vJTfO/1d+mMxnzjlefZO+zwyheeAKBUKbKxUuCVLzzB+w+Okt+/9OwN/v7LNwH4zpsN\nNE3lxvUtut3hmc2ApWKBarWMYcySuszOtQ0KhRy6prFSLlKtlEjp4YyTTEpnOjUQRYFUSkcQRdIp\nPRl3LMsy7Whq42AwurAg4Tx2rm2cWuQfaSysICCIAsr0WJDzLIn1sNveoVDIUSzm2d5ep7JSWihi\nx5HCMjr8SXn4XiRgOY/x6LgrvdvpI0si06nB/sHRQyn2v2i4Mh6XRP2whaZrpNI6rU7vQnn6GCe3\n9S0oU+Lt2e2QEhh5Qpoqc6ucZ7uQ4V5vxIORkcyXiLFzbeNjKxzGOOmJtSazpdvlv7SKuX98Ay0z\nIHH3bLGY57DRYjyeMhpNMKtfRm//4NwoZG2tstRz7c/pIY3HUzzXQ5IkyqUCtuMyHk+ZmRaO5yUR\nTLfTp32wzmM3xtRqVTRN4TtvNvjOmw2+/+Pj7v2daxsoikwhn2OjtoqsKJTLRVLpdCI5ktK1ZAGP\nDcr8FMbJ1MBxHKbDLqVodPA3XnmelejvX/7Ck+QzKb7xyvOsVfK89Mx18plU8ntzrcwP37kPQK0Y\nZpUty+Sg3mBtrYKuL4kyBYHhKPS00+kUuqaxf3BEq9WNtLI8vGhRm0TyG2FPQjgGt1TIJQtkKqUj\nK8rCHG7TslEV5ZEMyKPM0fhZYTYXWU2mUxzHPY46goD9/SPK5eJCAy0c1zx29w6TvzOZNNtb6xcm\nfvwi4KrmcQkEQUB15Ti0vEzOtt3pk4+6g2OYpsmt8nUm7gq3ucsWJySeJYlb5Tz7B0dY6QwHwwlb\n+UxS8M3nl0t4XBQH9Qaetyg2N1/cG9nHC0hh1WHYOvaws08XsepTpKyMXNC41xtxs5gLZeNddyGV\nFk9XazQ7SK6Ak/sCiq6gt3+Am948sxayc20jqYPEvy3LTmZjdNo9SuUCuB5TY4aqyNiWHdaFgiAc\nBARMjBli7gO+82bEkMIBFGpFmenM4jtvNlBlgV/+fGg8G802rufh+wGT8STsig6CiB4s4XoerYlJ\nQRYIgoDhaIJp2eF5p/QkcnpqO8e/+behskAhLfLFp47TTkdHrbC5bTRZ+PyLGZEnaqGCcVYO+653\nrm0lHry+RP1Y11SmUwNBENF1FV1XMS0LVVVQFZlUSqfd6YVSLPkchmEiicJxx3cQCvJZtk2lXGTW\ncUlF4pOBH7C6Uqbd6ZN+REZRzLq6Z+4uPH5L37ncC831QNi2Hc6w9wNkWUoiwiDwl3aJn4TjHF+j\n82QNP5bqF4Sk5mcYJumUBoKQKAgoivyx12we7J6tC/dpxJXxuAT8IAxZKyunb+CzYJkWiqJQXSkx\nHk8XjEdclM3KWcrjMq/xFl/MP3+KmbG9tc54PKXpeNzrj7lZDOW+LxP1xGg0OwS+n+SnY8SLcyzJ\n8aB/7OEXVp3kd2JAJBFtM8Pge0eIKYnMsyU+INxnO5teuLDi8+n1+vSFPqViEcOc8dj1LwOgdt4A\nUV5KD91cX2Xamy5duGaWiTJdZAaZlo0XSWYA1FYrjMZThp2w4/zWioskhQXLwPfJygJrOZ+9gcR3\n3mzwpScLSJLE+voqruvS6fSRZQlV09BUBSNwqE/vM3GOwCEZqjy+H5MITHgQe+gh5fbZzUVpi5js\n0B+MWFtdSaRPYuxc22A8nqIqMveiWfee7+N7Pt3+cIFtN5nOSKd1BsMRsiQRRLInqiIzm5lMpgYb\ntdXkt+P5aKrMQb1BOqVjzELNJ0EUUSWJ8WRKllAgcOfaBpOIiLBaLdNq92h3+khi2AgX9/Sch2Ix\nHzLEPmppbo6Npapn34Otdm+pzPq8kZgvTM/fBydHE7Ra3ZCBFhknWZbD+SeT6SPdfw/D9Z3TPWKf\nZlwZj0siltXw/eBMGZBudMOf5OTncsfsjNFoshA56JLGS9nP8frobTLNDM8+9cTCa+ZyGVTTYt+w\naI8mj3zhnjUd7aQX5Z7R/5PNWUzGcwYwYl4NvndE6vE8Wi3FfpQa2U5rjMZTKuUigiDwzNOPJ5FN\nPAAon8tC1ImuDN9FsgeY1S8nN7o1DlMLnu+zu3eIqsjR3IowVRZHTvXDJkEQyofEzCTf9xElKUnJ\nqLLAfKZ2fX01ib4eWwFbcLltv0tW0fBtn+5gyFSLjKgd/mTRqKXXYRrSc9/z73EruAHXQdd1TNMk\nk83SaPfY2VyNOpuPCRAQkQlcl15viKrK1A9bpzrCc7kM9x8cUEirDKY+pUIOURIpznVN7+4dsh0V\nqNO6TqlUoH7UxPeDMApz3DCFVW+EDZWdfigB72sosoIiK4DJdGokY4gt2yarhIvo/QcHiaESRAHT\nshaiQIJg6VyOZSgW8/RGA3z1+Lqy/OO0kYr6saV85md+OI6LObPwA3/hs4sdmnmDcnTUwvcDup0+\nlei8VyolZmY4nMqyLBzHxXYcSlGNbP47+KhwHJfgM9QmeGU8LgExYq5UKqXkZluGykMa+DQ97EY+\nmXLa2qzRaHZ4Jvskt9fe4/adu8kwovl99W6fUSqNdkLL56MiNmo71zYWahihQm4YeXgzn5pSZZS2\naBmLTJx5IyKmJPJfWmXfsECS0RyHdr3Bzs4msiyHdYmZmfSnxM1aE+0GWkFDMjtcLzoMe2FdRdAl\nrIl9HEXM1T10LTRkrrs4+S2uSxhzo1hTarg4eZ6PLAWnWDUFLUtJyOP7PrbpktZVns2HdOpGsxMO\nELIsnIGH67q4nse6WKO8dewgDIcjdN0lPdfsmdLjDnQRQRQTbTI7kgU/WQRXFIWjoxY3rm8h7h3y\nxnstvvJU+B6maTEYjDAtC1mSwv6P3gBBFBgMR2xvrYeLYGT8wwFXqzQaLQTBJaOk8fwA27YxLYuV\nShFVVbFsm1w2EypCjwysgY0ogGXadHvDhZnmlXKB/YOjpCfhoumbx/LXFlJX+/ZxDeUxZXuhqfai\nkirLIM4ZoXhu/WAwOjVIqtcbLDhhcT1pXqDQ830cx6VYTCMKp4Ukd65tXLhH6WHwPR9FkS/UWPxp\nwVXB/BIQhNBTMVw3YW/EUcbHAcuyyWbSbM42mNQmvH/3/qltYp2dsX155dyzcFBvJDeqKKlsZ48L\n8Ur5mDaad0JjVUyleHYjXDCerC3eNMVfXefZr1+n95cHWPUpeD4twyIolXgwMsJGyFyG1epx1BbP\nI7m/G87T9vQVAimFFjWgxbnpRquLEaViYpjRHOk4rSVH874VRWZ7KxziI4ghc+nWxg00LZxL5nk+\n0zlmkut6TKczJpFOkjt2uZG/dvw5SBLZTCqZ0Le5uUalUjrV1CUrCrZtJxPh4tebTAwEUcSyzIWI\n1fP8RBH3/oMDDMOk2xtgO+Gs8thbjmnUnhcarrSuJ9dg2LsSCiwe1BsUCnmCIGDn2kYo/RHl8V3X\nYzoxME2TUrmIpiqMJwbjcRjJDobjcMaFMQul/oXQEdq5tsFoPEGSRI6OWqiqgu8H6Jq2IOdxEZSN\nwvkbETpKj8Jcarej5s0TNGNJlCgUcom0iOu6oQMYBElkatk2vhcqDlimxe7eIYoiJwzhs4zZRzEc\ngR8kaUzDMBOj/1nAlfG4JCqVErokMYkWb8c7Pet4Hss44ycvwqOjFt1OP/HgNjfW2DY3GawMTg2O\nEUQBPB9TWn7Tzi+sF0Gz2U0u/nu9EX/4h/+a9967y7MbGzy7scEP/+xNfvhnb2J8aPEXf/lX/G/f\n/udIisrv/d7v8ezGBrIo8v6rr1L/6U95/9VXebJWwwbKv76FVksx+H6T0autpDckfp8PhxNarW5y\nk3c7YS3k9p27fPhgD09fIa0eIEZhfCGfpbZaIZdNL7DCCvksjUYb07SQZSmZEyGJIpOJkSy6muzy\ng78VyWZbVColJFkil8skAoYAiqqgaVo49yGlL+hAxVz+mAE2Hk/JZlKJlxuOjO1RjWi/8RjXrc1a\nWDuIBBMrlWM1ZEEQEmpsfC4xc0uSRCRZSgrks5kZaXEZ2I6LT+hsDAYjhsMxpm0jCKESged5eJ4f\n1k00jZlpUVutUFutoCihAJ8dGd3A99F1PTkvy7JJ6TrjyRTfP67PlIuFUA9KlBiPpqxUQiN6WUXf\nOI0r2gLrXpVb+g639J2laZ+TaSwnu4My2X0o3btaLeN5Ppqu0f3bP04ej2sgiiJDEEQqAjIIQlJv\nqq2tIEoSqqomUTiEkYjjuJ/IsCbHdaP3FUmn9YtmAT8VuEpbXQK+5yd6QaPpjMHMIn2O53XypljW\nib7sBlxfX2WdVV7rv0XJzC8YnJRjMZNS7O4domsaxUKWRqtLLRIZzJ5D4Z1nfq2tVQj8gA8GYSrI\ncRxe/MIX+KM/+iMePHgAwH/+j/4R//IP/oDf+Z3fwXFsDvZDVsi7777Ln/7pnybbZDSNP/qjP+Lf\n/Q/+vfCNJDFJZ1n1KbMPRlj7U7IvlpELGiNZYWRY3FRVKpUit9+9x/bWBrlcJkrbPU9++C5Se4Do\nVTCFGuOJQS5KoRQK+SSN0Gx2CQgS+qUsyziOS76Q57W/hu0nC3gDuH17lbW198IO8eEYQQjnZ4Tn\n7mJZFumUju240VwNB03SKJcLuJ6PLEmJwQuHRYWLfTaTSq4NAMt2Ftg+6ZSOZdt0u32GwxFBEKBp\nWmKcxuNp0i9SrZapHzaZzSxG47Be4gkpqis5RuMJsgzFQo7p1EimT47Gk4U0Zq/fT6jElikm148W\ndcsbs1BLyrTCORdx+kXTNGbWEDkrUSrmkyFhoiREv0UmxgzX8+l0B4+U71dayiMzlZzsDk52B3Vw\nG9EJHRJfyWMXnk7qLzvXNhYMxyk8wgo9GIweiXbc6w1ONf/NZhatdjcUYlRPSsF8dqzHlTDiJfD8\nY5ngL//Pf8zMtOhaDqVclnL6/LxsItt9bWNB4PAiaDht6r0jvrD2+eSx0WhCy/UpeO7CBX2StbMM\n4QyKbHLDL+vRkEYjNmpVPri/h6aqqJqKl8ny5Po60/EAWZGTvH2c733QHydF9lvlPK3JbIHqO4/J\nT3tIKRmnY5L/0rHhtJtNRFHkqSdvcnTUChcuER67fo1eb0DWa5BKZeiPwujKsU0UVUcX05h++Fgq\nlWY2M9CVKOfvmTwYZdid5NBkl+1ieN6iKLC6WmVmGDiOG3aOR+NkVUVGFCV832N9fRXLtGi0umia\nSm1tJeT3Z9OUy8VTi8NBvcHa6kqysMffSaPZwbJsrm2t02x1CIIAO3pfTVUJggDLshMdMwjz6+12\nD0/O8M79Pi8/nsO27KSmFtNR436WcrnIZDojm0kxHI4jravweph3PibTGYHv0+sPQ/qzadHpDlAU\nmakxY0MfYKdvMJlOWVtdwfP9xBC2Wl3SaR3H8dB1DVEgmtcuXmgS3nkU2keF4Dko47uIzghfSuEU\nniGQwoV5OBzj+8G5w5YMw8Q0zVOL/fzQKs/3CYKLzcyJNcemUwNNU+l0BwgIrK1V6PUG2LZzSpfu\na7/5u7z1weQzYUGuIo9LQBCFZMSoNzVxxhMmgb/gcS7DvJcV+EGilmvZbjLT/CzUlCp76gE/vneH\nz996GoiKwKqGNTd3/EL6VkGQeOo9w6Rnni76p8wZ+UqJD+7vsblRIwgCZEUm8AMsc8LuXh1VVVFV\ndaFQeL2USwxRrM+1SoqRebq4nn0uNHjKisbkpz3sukH+y1XUtTW20xrj8ZRUSqe2VuXD+3u8f/c+\nTzx+g/sPJtyobiEEYSG9eXBITs4yCgQksYTtuLhBmrErIQUiiqzgei6icov11Ju8+OKLvPGGwHPP\nHhfcRSFNsxWmZmLD4fl+NKI2/DxH42lY1I6MY0zdBcikwwgwndKpVstz/QKL6cra2gr3HxwsiAG2\n2z2q1XColO/7pDNFFEmkPxihRJRQY2YiS6ER7vWH5KJ61PxAsHK5yP7BEeOJge/7eG6BTCZFu93D\ndtxwzsT85x9dr5qmMh5NcSN5+zjtldUs7g36gEir00OSpIR1JcsSnudjR/pe2iV7PxqN9inNtJM4\nqDcIgiCphS3z3iFM/YWqJQHptI5dfOb4ySBAHdwGoDD/+BmwTIvp1FgaXcTvPRyOqVyQpBL4wbFs\nvxemyWLHY/41L+LwfVpxVfN4RPQtG2M2o9s9v2BeP2wymRoc1BvkcpkknXCe4YjxculFzIrJO+++\nDxzPQDZVPaG8xiMtH4aDwyb7+0e0JrOlhsNuLs4IKRRySJLEzDBxHIcH/TE3b+6AEEqUC4LA/QcH\nyfbzKrueHzUy6hq3yvmlM8vlgkb2uTLlX9/Cm7gMvnfEvmHRdDwOj5rUj5roKT0paJZLBcbj0HDM\ny4V4no/n+0iSGN2U4cJtWhau57G/D0ezHMPhouEYDsdIYjhSdmuzRjadQo1SR5Ik4Xke9cMm1WqZ\nG9e3ks99pVxM9J8gdA5OLjqO4zEYjKgftpLUVFz/gNCDj5sYi8U8xswkiDS3BEFE1VTSkTxKXJfZ\nubaB44RpsnnPN+wiD8imU2TSKUajCbIcUpqlJSml8TiczDebmfQGQ6y5An98HpIkUykX8DwPe45Z\naFk2hjFDEEhSaheBFc2E395ef6h22XRqRDNOjvs0Uqnlzpmmh6KQS50mQcAuPoPojKgfNqnXm9QP\nm6c0u9rtHoPBCFGSHpqWCokIFzMcrusu1GuW9ZzEOGk4riYJ/oIj9qx7+IkH+jDEC/LJdNVl6Igv\nl17kNd7igw92uXlzh9W0Rsuw0DU1eY3z8siaqlKtlpemqrKOTS2iBseLQsznH47GbG3WiG+t9VoV\nz/W4v3uAIsu8+94HrFYrlMtFbpXD8bP3B8MFYyKIQvL/smhE28wgZWUmP+2Rfa6MurZGNa2hKAqW\nbXH7zl02N2qYUxPbdzGXiPZ5XkirDNfBcEjT+sYGd+6AkoVXXwXI8SuvjBeipvg85/skHCec17Cs\nGU3TNTw/SPo3xuMppmlhzMxkrkcmmyabSZGKRs8CC+ma1dVKYghjxI5FrL4aq+A2m11U2ef1d1tU\n027YqR91hMcsM1WR8YMgmhsepifDek6OTjeUctd1NZk/Mpkakex8mSDwk1kj06mB7gZhz4xPwopz\nHJcg8CmXCvhBOMlvvbayUEQ+PGqdeQ3OX+cPEwBcJrkzHk+WDli6iNQ5LBdAdByX8Xhybh0jrlFe\nlFEV087PwlmfTzzP42qS4N8RlMvFc1NWMZbRDqVo9ORF8YRwk265F/aJRDfjvDrsw+C6YX1kQSV3\n/ljmOm6HwxE3b95ILvT4xnn/7n0sz6N+2GJ3/5CnnryJZduIokij2ebd9z4AjiOQs0QT42jkVjm/\nQAuWCxrZp4v0/jKMZvaNkJH05BNPsFIpUz9ssHV9h82NNYxZWOMQThQ/D49axKfiuA57e7tk1+8A\ncPPmXX7llTHt9qImWTadwjBMxpMpqytlNjdWcRyPRrPDaDReEKCMG/3SaZ10JHs+HI2pVsvsXNtI\n0oJKYjCWz8Zot3sMR2PuPzhISA5GJLQZSqX7IavK86lUCrx4M8/Q8EmndGprK0lEIYoipWIBQRTR\ndQ1d1xAEgWIxT6GQI5fLhLPlCdA1leFoHKa2PI/RaEI6rZNJp6Lz0VEUGV1VQlHIfCbpk2i1urTb\nfTrdAbPZjFw2zWRiMBqOk23gtJLuQb1xKZVaCGsFg8GIdrtHu91jdbXC/QcHp+i3DzMctm1j22f3\nYimRYY7ZZGdBlmV29w4TVV7HcQn8ICQdGCbD4ZjZLJpc2OwkigHzCsCxztvDEF8/nyVcGY+fAXq9\nAcuICcPBeMnWZ6NYzPNLhRd4e3Y7eWzzgh5RMzIyJ1VyIVzs251FI9RoLCr2vv/eXZ54/AbtSSg/\n/tj1bQaDEc88/fiC6ujtO3cByEcsknu90UP5+rEIZFmPPHxJpPzrWwy+dwSezweDMffvP2AwHKEo\nCt///ve5fecuqqLy3HPPoaoqtdoq6+sbvPPOO1QqJR482OPGjRtUqytUa5vcrKoU8+HxeL7ID19/\ng8l0xmQ64+69B/hBwGg8odcf0mh1Q8/fCifuuRF19qDeoH64KAkT1wGWSacc982Et1hsGOYX13g/\n07QwTYt2p4euaUyNGasrZRzHYb9+GI6vXRKhplM6nuvheh6KFNZhplFTZLfTTxZtSZIgCBJRRNdx\nmWmVKE0AACAASURBVM1MhNjKCgL9/oheb8B0auK5TsJoW4Z4sZzNLMy5lJYoCMjRfPbxaMp4NMXz\nfIzZ7FLy7KmURrGYp1otJ5HBjetbicz87t5B8lkmc+f9YEF2Pa7LnYdYcWG+8XQe8aIeRz6KImPM\nZpTLRdJpnUIhR0pXSaU0amsrqNGsjpjR1W73ku/kFw1XxuMSOGsRPCnZfBKZdIr6UfPU/g/rRD8L\ngiiQlTO88+77lPX/n703jZEkT8/7fnFn5H1n3dVHdfd0zwzn3OHyWO0ul2vSS0mWYUuULNmWZUjW\nBxECfKwOWAIFwQBhg5Bk2BAgiqAIk8KKNCVRlCmuSWp3ubuagzs7s3N093RXd51ZlfcZecTtD5ER\nlXX1VM+S2hl5HqBQVZlZmZFZEf/3/77v8z6PynbfOKZ2erKeHKrBLi9VzpQ5T4sCw+GIdDqF53rc\nvnOfmKadqpVfvxGUtMLgIytytHBcvbxGJp2OmsR333tAOalTnF1wDy4QJPPxGBv5NPLsost+ZpHJ\ntoFVnzDV4/ziL/4ii4sVVleWURSFf/dywPVPp1P84i/+X3zxi1/kyZs3OTysc/XqFX72Z3+WBw+2\nEAWR8tIaz13O8dWvfo0vfvGLfOITL/Iv/+W/4vXX3+Dq5TVKpTyiIFAsBP7jAgKSJDIaT1Bk5cgT\nQwi8rEOviHq9PSM/BAvtfrUW7Y53dg+O7ThDOmwyHmSqxmjMaDShUg4WHNOySSXjZDPBc4mSiKpp\nxGey/GEQMpwx7zY2OXRa5PNZCsUcpmVjjCeossRCpRj5fISOi5IokEwlmVoWtu1gjCcsLpaxbZv9\nao1eL9DFyuezpFLxwBNEFLEs+1TAexQEUWB5uUJcj0UOfIHTY+JIzuS7xPraEtlMJvJPCYO5IAro\neuBfHwpnnpd5WJZ1KhsKz/f528/KSmzbOVVae5T7X6mUxznDJyfEfMD7qOHj4PEYEEQBPJ+r2TTm\njN+/3zfely2hKAqiINIfBIto44KlpvNwK3WdUWVEbWcPCBrn83TNWq0ZlVq0mIYkSfR6gzODley5\npFIJVpYXuLcZTLRfubJ2rqPZaiZo/nmux0KliO/5NNtdptMpt25eI51OUcjnuH3nPlldO1bCepT3\nR4hLuVT0N/rV4LvxToe//rf/Z/7pP/0SIy3Gr/3ar9FuB59hrxuID/7lv/yX+da3v82tW0/w1a9+\njbW1NX7+53+e/+N//3tMjC7/01/7m/y5P/uT/JW/8lf4vd/7OtevX0fXdfaqtWAY0PcZDAxSqQSZ\nbBpZlmeT6hKmZSJJIsvlJBiHxL0eBbmP7rbICh1i5iGSsYciKwyMMVvb+6SScTrdXhS8Q0SyIaJI\noZCj2+tHvRPLsjmoNZFmsySiIMw8OqaPHFBTJBFNVY5JjM/D9XyazcAMS1UVMukknU6PRFwnO/O9\niOnB/MdgOCamqRzUAqp0Khm/cDklzK7n+wji/HkkCI/0oDl5foxG4yjDAZAklZieOtW4NoygdxTT\nUyytrAUGS1oyyDxKzxwNejrBoJ8gzA+Gxmb3BZPle9UahjFiNBpHWYltO1iWzXg8jcpztXprVsrr\n0Gh1qNVb7Owe0On0aDY70fd6vf3IqfH+YEis+Uo0+PhR0rb6eM7jMfDMlYT/1V/5OwAcDEaM5/SU\n8nin+P7zWjqSJJJOJc+UUf+gdL3Xum+QqCeIx5MslQtoMQ1jFFz8Zz2f43psn+h5eO32nJeDzmQy\n4dbNa8eooBeG7zOemIzGE0rFIIDcuH4FURCPZR/neZ+ferpweNH16PzOAbkfXUKY7RCtep3VlaWZ\nnEeHq5fXUBSFRquDaQZeFXFd552qzUbRoVQs4rpu1NheWizjjXtMp0EpR1JFMrpOpx8sRLo4JqEK\nSNYRm+6+uY7n+cT1GKqqIIpixPoCEN0xUucuEhaumqOvrNPpBtz+TDZNMqGzt3cYlcJEUWR1eYF2\npxc06R0bWZYj3bRwBmRqmpTLJb7xTosXrsTRYxoDY4zveZTLBYzRJLLP7Q+GCAiRp3k2m6bd6mLM\n3A5r9Ra5XJaDmZmWosjE9RiKrCDPHO2U/j22RxkScf3YTBAEdrMhLNtGj2kRC+2kjXD1oI7juGcG\nn/Msh8MAohhHUvWj0ZhEIs7A0iimFX7rW10KaQVZEnBcn8sLGoU4PGi42I5HKavQ7NksFVTSk/f4\nVmedF68l+fLrwf/y1lqcTELi5TtDskmZp5YcDGMUXb9hk3z+GB91jYazKydnWPb2D1lerJzqzSj9\ne0hWILHvI2CWvh8AeVLjU3/+H/Lmg9FHYs7j48zjMRDS7xzXo5zQyc/q0BKnGSQhLTP8WlooHxPp\nm0f1oMFgYES2rRfFS7nnGFVGDK1+IEJIwOi6aCBKz7w3ABKJRLArncnEh33o0MTpLPzml97gN7/0\nxtENgkA8HmM8HgUijzevcXDY4KDWOBYwLpKBwBxDa9YH6X+9HsyF1OsoxRKpVILRaEQhn+fB1i6j\n8YSJN53pW8E7VZsrFQ1Jkmm2OqRSiaiGXW+0qPUtOhORoaMSTy/RMVXGXgxTSNB28tiZGzRjTzMt\nfZJp6ZPIkoQkiYGPumUzGBrHdtKeFMcuPc+09EnszA000WZD3aakdjGMEeNxcGwrywuUSoVoYdLj\nOuPJBNt2KBfzQWDzPCQxeK1cLossq1S0a1QWlkimM5SKuYgsoEgiruvS6fZZWV4gnU4e61kYM68T\nAAGBWq1BpVykPJPyGBpjRpMJ7U5g4etak8go6qAWUI3PKl8pinxsyO1kqfM8m1c4zToKd+1h/8tO\nHh1/WCb6zsMRv3/P4AufCDKbFzaSvHQ9QbNnIykqwmiPJ9eD359cT/CwZiKpOt9/I8WbD0d84RN5\nskmZS5UYd/YmfOETeTzPZzSaHLt+Q8WHi2ZcocpA9aB+rOy1urIYBY7JxATfJ9Z8BTtznWnpk0wK\n38+2czn6XGVj+5ElsA8bPg4eHwDbfQNZEsnHg7LM5Zlh03kwpybVw/qxdHke62tLpNNJ5A8weRvO\ngJiN2vsuyidVRVuHNW7e2OD6xmVGoxHLS5Xogg8XppCt8zhYXwuE/hzbYXV5gcVKidt37p8KIBcV\nvgv/LpQ6sRoOgiSy2RmQLBdYXChx6+Y19vYPEG2R/thlsxV8lhk96FeEMyLhl+t6UY9GEoP+hmUF\nO/9MOsniYpmt7X063T693oCd3QMWF8vkc4EIYamUR59tHnq9s50UG90Ju/41mlaOuNsmP3qTNXkf\nz3WJz5R2W+0ujm2ztFAipqkMhoHml6LICIJAu9VFkUS+8Y0EK6setm0zmTHNxjO9q2a7G4hqxnV6\nvQGSJJ2a4s7nAkHCRDKOpirYto3n+iyUC5RLeZIJnaWFcjAoyQTP8xmPJxTyuXNZTe6sRxZCEISo\nFxRCfAyZ9ZCWnJyRLUJplBCfekLmmTWYTobRd3M64mrZYzoZBrLxk2H0+5PLPvQfsre7Q1lts7e7\nw/OXgseE31+8IuJfYLZCEs9nRobN9Evry2fOaXQ6PbKjt5Enh0xLn4xuF0SBxcUy62tLTCYmnpo9\nt1z8YcRH50g/BHA9n63OgIXkaXru6sriubsGLaaxvFi5UBnorIXo/RBmIF67/cgA0jxxMd66eY2t\n3T0ebu+RyaRxbAfHObLiPItieR7V9yQWKkXuP9gK6K5Dg1s3rwUBJJeKmFg/9/P/BFFSqdfbaLEk\noiCixZIYozFvvX0HUVJ56+07mJbDk0tL+J0O3/8fPUXucoaiCNdLJbbv3ANRoW8F5TbLssA+mp/I\n57Osry1FMxThVyadRFFklpYWSCYTQblGkXEcB2tGx4RgUjzsSezsHhCPxzBGY4bDEaIo4npeMGzX\n6R3rbUiiQCaTxjQtHNel6ySYlj7JlrWEOGkQ77xGcvgui/FxEIREKXIWLBaypJJBJlgo5tBiGp/9\n9Ii3tx+yd9BGjmi6QWM/3BQUijmSyTjD4Yhms8NkMqXd6pLPZag12lSrdSbjCY7rRv2JZrtLq9Wl\n3elzWG8S0zSGmee4tFJGlmV6vT6ee+SFEdrTplMp8tksmfTRhsCybLLZ9LHz/CSVeh5n9XEqlQKH\ns4Z3dWJRvcD5FopDppIJer0B7VaXdqtLvd7mwFmIWFunZjpmn0HYJwx7JxfBeb0bWQ6EF9Xu28Sa\nr6C1vkUhm8AsvIATP8pkRicETNudLlbmCfD/4MUX/7Dw8ZDgY0ASBVRJQpq7IDY7A3KaSiERQ4Rj\nRjLzuHvvAdevXUaW5XPlFgD6A+OUZ8BFEA4R5v0Cm50BsiBwKffoiVhzalIuFVEkiVanhzHTY4Kg\nJHHWjnMheXFdrptPHPmR7OweRAGkVCxwtZBj4b/4SX7pl36ZjY0NDMNgv1rl5Zdf5kd+5EcA+IVf\n+Cc899xzpNNZbNvi4OCAr3zlK8Tjcf7En/jjfOUrX2Fj7Ram7RGTkmx2umxcWecb7zQQsfBQZmKG\nwfvQYsnZ+zYolct0Nw9Ip7MYw2CIrrKwxIMHm6RSCfarNWKaSqUcNE0brQ6JuM7e/iGiIJLLZTCM\nEZl0itF4wmg0IRaLUa8Hi3uhmItq5rV6i8lkytb2PqIo0rHijLRbyIpCt9ujnBqSsTZJouLGL0ez\nO/Nlk99+PSht3K0a+I6BIitkM2kGg0Aq3Z0txLIsUyrmIsOtTCaFaTmIYqCpVD1osFAuUGu08TwP\nVVEQRAFvZrErSyKeB4I7CQQaq3UOa00c10aP6cHMxdYOgzMIdI+rWaXMZG8EUYjea683YH11kb5p\n0RybTFyPat9gec5++SROXi+i2cHTwkBRYDgYnT3lPfd8mUwKXQ+b5+f3NyYTE9/3z1cT9n2U0Q5W\n7unoptCGd/45BEEMstNmm0vry1TKRfp9A4THm/36XuLCDXNBECTgW0DV9/0/KgjCTwN/EQg1w/+m\n7/u/ecbfZYF/DDwF+MBf8H3/ZUEQ8sA/Ay4B28Cf8n3/D84c4w8Bz15J+r/5f/41tlsDFhM6h6MJ\nN28GZR7DsonLMqIoUKu3Tjn2ea5HvdF6pIR1o9GmWMgFqX7oyTy7uC6CXm/AbqtKurByzAnwvKE9\nfTpheakSeE6Mp8d2ZrUZ68dxHPK5DFpMO/KinvmV/5tfeROAL/zp5y50fKF74t7eIUPD4KlbNxi7\nIEsSguugxnREz0WUg8zE82a7MM/HGI/QNZWJI6CrMrV+H13VKKZT3DuY0jNs1rNJpgTN5tHUpZRR\n+M7DEdeWdXKxCXdrEpWsym7TZCUv0Rx6rGeTVMoCv/NGj2vLRxllXBMZmx6jqcuttTgPHz5geakS\n/T/CCfVms4PjOLieF5XHbNuOdKYCZV6X8WSCKIqB1e/eIbIskUqnIhHDUEa9ko/Rq++RSwdBejIZ\nIcgJmr0RQ0thf5TgU6u9Y818O3WNl6s5XrgcDPi5fsDw8zyH6dRCURRarR6VSg7X9ZmMBuwPdIyp\nx3PrIrYbSJXURzpXyzOfd3uIpwSbj9A5s1TMRSq8Z6FWa54S+juvMX4S8xP/8+dZiDQe5UdMps/j\nePAI8IclyHgSseYrUWnKcz36gyG53PkeJsPBiE6vH31Gn/3Jn+bNj4gw4uOUrf4qcOfEbX/P9/1n\nZ1+nAscM/wD4Ld/3nwCemXuOvw78ru/714Dfnf3+4YYIRhwyy0lacY/VTBJvVspKqgoPZ4wiRTq9\nexAlkYVK6dTt8yiXCwiCwHhisl+tUa+3Lxw4Xtt6A+ISa2trZBMixaQUffWsET1rFP1uGAHF158Z\n4QQsnaMehOd6mJZNOpVAEEUURaF6UKc/MPA9n418mkNjjOM+XnMv7J0IosCl9VXeuf0eMdFjv9Nh\nq9fnvVqNO40m7x4cYE4NbGuKbU2ZTEfs7Ozh+R6a5FI7PMDsdakbQ949OGC3PuW5y0n6jonp+Ow2\nTUzb5+7+hKsLCutljW+80+LWShA4CimZ/Y5LMiahJ6ZsHQYBZr2ssV7WGE1d7u5P2G2aGFOXb989\n5F7N4utv1bm9HWQFghAEEGVmixva4fb6g6hUFtNUhsY4MqzKZlIRLdidE060ZurDpmWxVx8ylgo0\npknGcpmeX2Lop1i6/AT7o2D3rJaewM7coKbcYtO6hJxd5wdupvh371m0R/DObhBA3UmHeDzO194Z\nsdnR+M6WiaIopLMFDtoWn7ie5N++a/GdXY/6SKfRd+h0escMzryZblilUjh2zGdhvqe3X63NMq2L\nnb+CINBsBgykUE14vkc2QDzlbXPuc3mnSz+CcHoG6lF4v9mWw8NGRCKY13dzEkcGYq7nkctlTs2B\nzT/3fOCAj5Ig+wXLVoIgrAA/AfwvwH9/0ScXBCED/BHgzwP4vj9zggbgPwE+M/v5F4GvAn/tos/9\nvYDv+fi2w9JM5O/hu3t4MTc6ycPvhWIuKhnMQ5ixm85KicMdmiAKM6mIBTzXu5CE+04nOBnvVe9d\n+L0UkxKGlIyOxXHcKFDd29xC13W6vQGmZVLdzfHK129y7ck72MkhG/k0S+kEggAT+9FmWGdhZXlh\nNri4wL37W2iaRkKRGWlHO9r50tu9+1vcunkt2p0WCzmarS6MRyRyOaiMudcMasg7v9+mnFeYtKbk\nv7/M2Id3D/qsX4qz02uzURbZ3O0zMj1GfdidXfcJTaRTHXPjxg3M9nskCajLqqygKgrL2Qy249Jo\nmnzl23t4KKiywKeW0+zsHrB2Qs9ob/8QRZbJ5zKoisxBrRmx7ZaXyoxGE0RBODY7IMtytNAYxghJ\nllBVhXQqiW07PHcpxhvb08BLo9Uhl0lhmkHgkUWBTz+VoNNu8tzVJWzbpufkyNo2P/pcFtsco2hx\nvPrrYI349K0X8T2HH38hh+e6iJLELc9j0LeD6XgnqP/3+oMoi1YUGe8R5mfzMwqSKOI/hvxOSNTo\ndHrkshnGkymHhw3WSgV2B8GxGKp2IQr52Ohj2YljpSpZDuRITpaQdnYPEEUBQRCQRJHFxTLDwYjV\n5QV2dg9QZ5uDk5i/bX4Y1FODLCOUYw/P2cPDRjTR7/t+sDEUALwPRov/EOCiedzfB74InCyi/5Qg\nCP8VQTnrfzij7HSZoKz1C4IgPAO8DvxV3/dHQMX3/ZCiVAPO5PUJgvCXgL80+/Uf+b7/jy54zH/g\nEEQBfa4PMI3p3JrbHU1sB322ECSTiTN7G4OBcWa/46zUXpRE2u3e+wYP+USmk9JTXFu6huDA6zvf\n5vrydSrpCo7n4Lser2y+ymHnEEUtR1x8gPv3NrHdwM50NBo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arCFs207kUb6wUKLd6tJs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XTSUEgF2ddqL8PUsdP7Prvt7XI4dSxJErGYFjLsBl+8TOc/fj1QVTbH0oQ7QD41Se6Xvh8IgnU2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q0tMYrFIju3xylFo4T9QRBZ/H4f0XgY3clgff3UZa5cmSAWCVeZGsAOjx2amWd7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\nhUKhqBulPBQKhUJRN0p5KBQKhaJu/g90HIcAZR3ZoAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9572e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clusterClubs(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
